Machine Learning and Optimization for Cutting Edge Applications in Digital Music

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In this talk, I will explore how state-of-the-art machine learning and optimization techniques are currently being applied in the field of digital music, with a focus on music generation and hit song prediction. Music generation systems are becoming increasingly important, bolstered by rising global expenditure on digital music which was over 64 billion USD in 2014 alone. Most music generation systems are based on statistical models and rules. A drawback of these systems is their inability to generate music with global structure. Music without long-term coherence will fail to hold the listener’s attention. In my current EU project, MorpheuS, optimisation algorithms are used to constrain the structure of generated music by incorporating pattern detection techniques. Music is then optimized to fit a specified level of tonal tension that changes dynamically throughout the piece, thus making it suitable for applications in game and film music. Deep learning methods are currently being incorporated into the system the further improve the sound quality. In the second part of this talk, I will zoom in on the dance hit song prediction problem. With annual investments of several billions of dollars worldwide, record companies can benefit tremendously by gaining insight into what actually makes a hit song. In my research, I constructed a database of dance hit songs from 1985 until 2013. This was used to build a successful SVM and logistic regression-based “top-10” hit prediction model.


Dorien Herremans is currently a Marie sklodowska-Curie Postdoctoral Fellow at the Centre for Digital Music at Queen Mary University of London. She is currently working on the project: “MorpheuS: Hybrid Machine Learning — Optimization techniques To Generate Structured Music Through Morphing And Fusion”. Dr. Herremans received her Ph.D. on the topic of Computer Generation and Classification of Music through Operations Research Methods. She graduated as a commercial engineer in management information systems at the University of Antwerp in 2005. After that, she worked as a Drupal consultant and was an IT lecturer at the Les Roches University in Bluche, Switzerland. She also worked as a mandaatassistent at the University of Antwerp, in the domain of operations management and research. Dr. Herremans’ research interests include machine learning and optimization techniques, with a focus on novel applications for digital music and audio.